{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pairwise-learning-for-neural-link-prediction","title":"Pairwise Learning for Neural Link Prediction","arxiv_id":"2112.02936","date":"2021-12-06","proceeding":null,"authors":["Zhitao Wang","Yong Zhou","Litao Hong","Yuanhang Zou","Hanjing Su","Shouzhi Chen"],"abstract":"In this paper, we aim at providing an effective Pairwise Learning Neural Link Prediction (PLNLP) framework. The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.e., neighborhood encoder, link predictor, negative sampler and objective function. The framework is flexible that any generic graph neural convolution or link prediction specific neural architecture could be employed as neighborhood encoder. For link predictor, we design different scoring functions, which could be selected based on different types of graphs. In negative sampler, we provide several sampling strategies, which are problem specific. As for objective function, we propose to use an effective ranking loss, which approximately maximizes the standard ranking metric AUC. We evaluate the proposed PLNLP framework on 4 link property prediction datasets of Open Graph Benchmark, including ogbl-ddi, ogbl-collab, ogbl-ppa and ogbl-ciation2. PLNLP achieves top 1 performance on ogbl-ddi and ogbl-collab, and top 2 performance on ogbl-ciation2 only with basic neural architecture. The performance demonstrates the effectiveness of PLNLP.","url_abs":"https://arxiv.org/abs/2112.02936v6","url_pdf":"https://arxiv.org/pdf/2112.02936v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pairwise-learning-for-neural-link-prediction","repo_url":"https://github.com/zhitao-wang/plnlp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"pairwise-learning-for-neural-link-prediction","repo_url":"https://github.com/zhitao-wang/PLNLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"learning-to-rank","task_name":"Learning-To-Rank"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"link-property-prediction","task_name":"Link Property Prediction"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"property-prediction","task_name":"Property Prediction"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-property-prediction-on-ogbl-citation2","task":"Link Property Prediction","dataset":"ogbl-citation2","model":"PLNLP","rank_in_archive_order":13,"of":23,"metrics":{"Ext. data":"No","Number of params":"146514551","Test MRR":"0.8492 ± 0.0029","Validation MRR":"0.8490 ± 0.0031"},"uses_additional_data":false},{"leaderboard":"/sota/link-property-prediction-on-ogbl-collab","task":"Link Property Prediction","dataset":"ogbl-collab","model":"PLNLP (random walk aug.)","rank_in_archive_order":5,"of":34,"metrics":{"Ext. data":"No","Number of params":"34980864","Test Hits@50":"0.7059 ± 0.0029","Validation Hits@50":"1.0000 ± 0.0000"},"uses_additional_data":false},{"leaderboard":"/sota/link-property-prediction-on-ogbl-collab","task":"Link Property Prediction","dataset":"ogbl-collab","model":"PLNLP (val as input)","rank_in_archive_order":8,"of":34,"metrics":{"Ext. data":"No","Number of params":"35112192","Test Hits@50":"0.6872 ± 0.0052","Validation Hits@50":"1.0000 ± 0.0000"},"uses_additional_data":false},{"leaderboard":"/sota/link-property-prediction-on-ogbl-ddi","task":"Link Property Prediction","dataset":"ogbl-ddi","model":"PLNLP","rank_in_archive_order":8,"of":31,"metrics":{"Ext. data":"No","Number of params":"3497473","Test Hits@20":"0.9088 ± 0.0313","Validation Hits@20":"0.8242 ± 0.0253"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2112.02936","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.02936"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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